PhD in Statistics
The PhD Statistics Programme is primarily a research-based post-graduate programme. The programme has a 3-year duration (maximum 4 years) during which the student will be generating new knowledge and considering that new information concerning existing information. The programme interprets the term “statistics” very broadly and permits specialisation in probability, statistical theory and analysis, biostatistics, and interdisciplinary areas of application. Qualified students would typically have a strong background in mathematics. The student is expected to carry out a programme of research under the supervision of a primary supervisor and one or more co-supervisors. The programme offers opportunities for PhD research in several areas of statistics including: 1. Pure Statistics 2. Data Science 3. Applied Statistics 4. Bayesian Statistics 5. Statistical Theory 6. Applied Probability

Key Information
Academic Division
Tali Graduate School
Duration
4 Years
Awarding Institution
University of Cape Coast (UCC)
Accreditation
Ghana Tertiary Education Commission (GTEC)
Institutions
Teaching Institution
SouthShore University College
Awarding Institution
University of Cape Coast (UCC)
Quality & Accreditation
Fully accredited by GTEC and monitored by the DOEQA. External Examiners ensure global assessment parity.
Admission Requirements
- Entry Requirements
A. Candidates with at least a Master’s degree in Statistics can be admitted directly into the PhD programme. B. Candidate with a Master’s degree in Actuarial Science, Mathematics or Mathematics/Statistics related discipline having CGPA of 3.0 or better, with no more than one grade C+ or lower in Research Methods can be admitted directly into the PhD programme but must audit some selected prescribe courses from the MSc Statistics programme.
- Application and admission into the Doctoral programme
A student will apply in writing through The Deputy Registrar, The Graduate School. The Dean of TGS will consider the application with the Graduate Admissions Committee and issue admission letters.
Curriculum Overview
Year 1
Semester 1
GSST 551: Advanced Statistical Methods
GSST 553: Probability Theory and Distributions
GSST 555: Advanced Design and Analysis of Experiments
GSST 557: Regression Analysis
GSST 559: Environmental Statistics
GSST 561: Longitudinal Data Analysis
GSST 563: Applied Time Series and Forecasting
GSST 565: Life Contingency I
GSST 567: Economic Statistics
Semester 2
GSST 568: Statistical Inference
GSST 570: Research Methods
GSST 572: Bayesian Statistics
GSST 574: Multivariate Data Analysis
GSST 576: Applied Stochastic Models
GSST 578: Econometrics
GSST 580: Life Contingency II
GSST 582: Demographic Models
Semester 3
GSST 571: Survival Data Analysis
GSST 573: Advanced Sample Survey
GSST 575: MSc Dissertation
GSST 577: Operations Research
GSST 579: Actuarial Models
GSST 581: Financial Mathematics
GSST 583: Epidemiology
GSST 585: Population Analysis and Official Statistics
Year 2
Semester 1
Research Methodology and Data Analysis
Advanced Academic Writing
Seminar
Thesis
Semester 2
Thesis
Year 3
Semester 1
Thesis
Seminar I
Semester 2
Thesis
Seminar II
Year 4
Semester 1
Thesis
Seminar III
Semester 2
Thesis
Semester 4
Seminar IV
Career Prospects
Academia
Government
Marketing Services
Financial services
Healthcare services
Research and development companies
Bio-pharmaceutical industries
Not-for-profit sector